📈 Stock Market Prediction Web App

This project is a full-stack stock market prediction platform built to bridge the gap between machine learning models and real-world user experience. It enables users to explore historical stock price data and view future price predictions generated using data-driven models — all through a fast, responsive, and intuitive web interface. Rather than focusing solely on prediction accuracy, the goal of this project was to design a production-style system where data flows cleanly from model to backend API and finally into a polished frontend. The project reflects my interest in applied machine learning, scalable backend systems, and clean, user-focused data visualization, instead of isolated or experimental ML notebooks. 🔴 Live Demo: 👉 https://stock-market-predict.vercel.app/

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✨ Overview

This application demonstrates an end-to-end ML-powered web product, combining data collection, preprocessing, predictive modeling, backend API design, and frontend visualization. The core focus areas were:

• Maintaining a clean frontend–backend separation

• Integrating machine learning in a practical, usable way

• Delivering a responsive and accessible UI across devices

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🖼️ Preview

Desktop Experience

Mobile Experience

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🔗 Project Repositories

| Layer | Repository | |-----|-----------| | 🎨 Frontend | https://github.com/XyonX/stock-market-predict | | ⚙️ Backend | https://github.com/XyonX/market-predict-backend |

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🚀 Key Features

• 📊 Interactive visualization of historical stock price data

• 🤖 Machine learning–based prediction of future price trends

• 🌐 RESTful backend API handling data processing and inference

• 📱 Fully responsive design optimized for desktop and mobile

• 🔄 Dynamic data fetching with real-time chart updates

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🧠 System Workflow

1. The user selects a stock from the frontend interface

1. The frontend sends a request to the backend REST API

1. The backend:

• Fetches and preprocesses historical stock data

• Runs the processed data through the prediction model

1. Predicted values are returned to the frontend

1. The frontend renders predictions using interactive charts

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🛠️ Tech Stack

Frontend

• JavaScript

• HTML & CSS

• Charting libraries for data visualization

• Responsive UI design

Backend

• Python

• Flask (REST API)

• Machine learning models for prediction

• Data preprocessing and inference pipeline

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💡 Project Highlights

• Real-world application of machine learning within a web product

• Clear, scalable API-driven architecture

• Separation of concerns between ML, backend, and UI layers

• Mobile-first responsive design

• Deployed and served using Vercel for a production-like setup

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🔮 Future Enhancements

• 🔐 User authentication and personalized stock watchlists

• 📈 Comparison of multiple prediction models

• 📉 Confidence intervals and risk analysis metrics

• ☁️ Cloud-based deployment with automated CI/CD pipelines

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📌 Summary

This project showcases my ability to design and build production-oriented, ML-powered web applications, covering the complete lifecycle — from backend modeling and API design to frontend visualization and deployment.